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Bayesian exploratory factor analysis

Author

Listed:
  • Gabriella Conti

    (Institute for Fiscal Studies and University College London)

  • Sylvia Frühwirth-Schnatter

    (Institute for Fiscal Studies and Vienna University of Economics and Business)

  • James Heckman

    (Institute for Fiscal Studies and University of Chicago)

  • Rémi Piatek

    (Institute for Fiscal Studies and University of Strasbourg)

Abstract
This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identifi cation criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confi rms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.

Suggested Citation

  • Gabriella Conti & Sylvia Frühwirth-Schnatter & James Heckman & Rémi Piatek, 2014. "Bayesian exploratory factor analysis," CeMMAP working papers CWP30/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:30/14
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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